Use this metric to quantify prediction error or classification quality only after matching the target scale, aggregation, weighting, and statistical assumptions to the evaluation question.

probabilities

Required list input.

outcomes

Required list input.

Compute mean squared probability error for binary outcomes. Probabilities must lie in [0,1], outcomes must be 0 or 1; lower scores are better. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

score

The resulting score returned as a number.

• Different metrics penalize errors differently and can be dominated by outliers, scale, imbalance, zeros, or threshold choices. A single aggregate hides subgroup, temporal, and distributional failures.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Report multiple complementary metrics with uncertainty and subgroup breakdowns, inspect residuals, and compare against meaningful baselines.

• Mean Squared Error Calculator

  Calculate mean squared prediction error.

• Root Mean Squared Error Calculator

  Calculate the square root of mean squared prediction error.

• Mean Absolute Error Calculator

  Calculate mean absolute prediction error.